optimize batch_norm & pool op for kunlun (#30490)

paddle/fluid/operators/batch_norm_op_xpu.cc

@@ -139,16 +139,14 @@ class BatchNormGradXPUKernel : public framework::OpKernel<T> {
     auto* dscale_data = dscale->mutable_data<T>(ctx.GetPlace());
     auto* dbias_data = dbias->mutable_data<T>(ctx.GetPlace());
     auto& dev_ctx = ctx.template device_context<DeviceContext>();
-    int r = xpu::batch_norm_backward(dev_ctx.x_context(), N, C, H, W, x_data,
-                                     dy_data, scale_data, saved_mean_data,
-                                     saved_inv_variance_data, dx_data,
-                                     dscale_data, dbias_data);
-    PADDLE_ENFORCE_EQ(
-        r, XPU_SUCCESS,
-        platform::errors::External("XPU API(batch_norm_infer_forward) return "
-                                   "wrong value[%d], please check whether "
-                                   "Baidu Kunlun Card is properly installed.",
-                                   r));
+    int r = xpu::batch_norm_grad<T>(dev_ctx.x_context(), x_data, dy_data,
+                                    dx_data, N, C, H, W, scale_data,
+                                    saved_mean_data, saved_inv_variance_data,
+                                    dscale_data, dbias_data, true);
+    PADDLE_ENFORCE_EQ(r, XPU_SUCCESS, platform::errors::External(
+                                          "XPU API(batch_norm_grad) return "
+                                          "wrong value[%d %s]",
+                                          r, XPUAPIErrorMsg[r]));
   }
 };
 

paddle/fluid/operators/pool_op_xpu.cc

@@ -30,6 +30,7 @@ xpu::Pooling_t XPUPoolingType(const std::string& pooltype, bool exclusive,
         "Pool op only supports 2D and 3D input."));
   }
 }
+
 template <typename DeviceContext, typename T>
 class PoolXPUKernel : public framework::OpKernel<T> {
  public:
@@ -41,7 +42,6 @@ class PoolXPUKernel : public framework::OpKernel<T> {
     std::vector<int> strides = context.Attr<std::vector<int>>("strides");
     std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
     bool exclusive = context.Attr<bool>("exclusive");
-    bool is_test = context.Attr<bool>("is_test");
     bool adaptive = context.Attr<bool>("adaptive");
     PADDLE_ENFORCE_EQ(
         ksize.size(), 2,
@@ -60,36 +60,32 @@ class PoolXPUKernel : public framework::OpKernel<T> {
         ksize[i] = static_cast<int>(in_x->dims()[i + 2]);
       }
     }
-    const int c = in_x->dims()[0] * in_x->dims()[1];
+    const int n = in_x->dims()[0];
+    const int c = in_x->dims()[1];
     const int in_h = in_x->dims()[2];
     const int in_w = in_x->dims()[3];
-    const int out_h = out->dims()[2];
-    const int out_w = out->dims()[3];
-    const int win_h = ksize[0];
-    const int win_w = ksize[1];
-    const int stride_h = strides[0];
-    const int stride_w = strides[1];
-    const int pad_up = paddings[0];
-    const int pad_down = paddings[0];
-    const int pad_left = paddings[1];
-    const int pad_right = paddings[1];
     const float* input = in_x->data<float>();
     out->mutable_data<T>(context.GetPlace());
     float* output = out->data<float>();
-    xpu::Pooling_t pool_type = XPUPoolingType(pooling_type, exclusive, is_test);
     auto& dev_ctx = context.template device_context<DeviceContext>();
-    int r = xpu::pooling_forward<float, float>(
-        dev_ctx.x_context(), input, output, index_data, pool_type, c, in_h,
-        in_w, pad_left, pad_right, pad_up, pad_down, win_h, win_w, stride_h,
-        stride_w, out_h, out_w);
-    PADDLE_ENFORCE_EQ(
-        r, xpu::Error_t::SUCCESS,
+    int r = xpu::Error_t::SUCCESS;
+    if (pooling_type == "max") {
+      r = xpu::max_pool2d(dev_ctx.x_context(), input, output, index_data, n, c,
+                          in_h, in_w, ksize, strides, paddings, true);
+    } else if (pooling_type == "avg") {
+      r = xpu::avg_pool2d(dev_ctx.x_context(), input, output, n, c, in_h, in_w,
+                          ksize, strides, paddings, !exclusive, true);
+    } else {
+      PADDLE_THROW(platform::errors::InvalidArgument(
+          "Unsupported pooling type for kunlun ", pooling_type));
+    }
+    PADDLE_ENFORCE_EQ(r, xpu::Error_t::SUCCESS,
                       platform::errors::External(
-            "The pool2d XPU API return wrong value[%d], please check "
-            "where Baidu Kunlun Card is properly installed.",
-            r));
+                          "The pool2d XPU API return wrong value[%d %s]", r,
+                          XPUAPIErrorMsg[r]));
   }
 };
+
 template <typename DeviceContext, typename T>
 class PoolGradXPUKernel : public framework::OpKernel<T> {
  public:
@@ -126,47 +122,33 @@ class PoolGradXPUKernel : public framework::OpKernel<T> {
     if (!in_x_grad) {
       return;
     }
-    const int c = in_x->dims()[0] * in_x->dims()[1];
+    const int n = in_x->dims()[0];
+    const int c = in_x->dims()[1];
     const int in_h = in_x->dims()[2];
     const int in_w = in_x->dims()[3];
-    const int out_h = out->dims()[2];
-    const int out_w = out->dims()[3];
-    const int win_h = ksize[0];
-    const int win_w = ksize[1];
-    const int stride_h = strides[0];
-    const int stride_w = strides[1];
-    const int pad_up = paddings[0];
-    const int pad_down = paddings[0];
-    const int pad_left = paddings[1];
-    const int pad_right = paddings[1];
     const float* input = in_x->data<float>();
     const float* output = out->data<float>();
     const float* output_grad = out_grad->data<float>();
     in_x_grad->mutable_data<T>(context.GetPlace());
     float* input_grad = in_x_grad->data<float>();
-    xpu::Pooling_t pool_type = XPUPoolingType(pooling_type, exclusive, false);
     auto& dev_ctx = context.template device_context<DeviceContext>();
-    // Need to init memory in the first place
-    const int zero = 0;
-    int r =
-        xpu::memset(dev_ctx.x_context(), reinterpret_cast<void**>(input_grad),
-                    zero, in_x_grad->numel() * sizeof(float));
-    PADDLE_ENFORCE_EQ(
-        r, xpu::Error_t::SUCCESS,
-        platform::errors::External(
-            "The Pool2d XPU OP return wrong value[%d], please check "
-            "where Baidu Kunlun Card is properly installed.",
-            r));
-    r = xpu::pooling_backward(dev_ctx.x_context(), input, output, index_data,
-                              output_grad, input_grad, pool_type, c, in_h, in_w,
-                              pad_left, pad_right, pad_up, pad_down, win_h,
-                              win_w, stride_h, stride_w, out_h, out_w);
-    PADDLE_ENFORCE_EQ(
-        r, xpu::Error_t::SUCCESS,
+    int r = xpu::Error_t::SUCCESS;
+    if (pooling_type == "max") {
+      r = xpu::max_pool2d_grad(dev_ctx.x_context(), input, output, index_data,
+                               output_grad, input_grad, n, c, in_h, in_w, ksize,
+                               strides, paddings, true);
+    } else if (pooling_type == "avg") {
+      r = xpu::avg_pool2d_grad(dev_ctx.x_context(), input, output, output_grad,
+                               input_grad, n, c, in_h, in_w, ksize, strides,
+                               paddings, !exclusive, true);
+    } else {
+      PADDLE_THROW(platform::errors::InvalidArgument(
+          "Unsupported pooling type for kunlun ", pooling_type));
+    }
+    PADDLE_ENFORCE_EQ(r, xpu::Error_t::SUCCESS,
                       platform::errors::External(
-            "The Pool2d XPU OP return wrong value[%d], please check "
-            "where Baidu Kunlun Card is properly installed.",
-            r));
+                          "The Pool2dGrad XPU OP return wrong value[%d %s]", r,
+                          XPUAPIErrorMsg[r]));
   }
 };
 

paddle/fluid/platform/device_context.cc

@@ -172,16 +172,7 @@ Place CPUDeviceContext::GetPlace() const { return place_; }
 #ifdef PADDLE_WITH_XPU
 XPUDeviceContext::XPUDeviceContext() { context_ = xpu::create_context(); }
 
-XPUDeviceContext::~XPUDeviceContext() {
-  xpu::destroy_context(context_);
-  void* l3ptr = nullptr;
-  int l3_size = 13.5 * 1024 * 1024;
-  xpu_malloc(static_cast<void**>(&l3ptr), l3_size, XPU_MEM_L3);
-  if (l3ptr != nullptr) {
-    context_->_l3_mgr.set(l3ptr, l3_size);
-    std::cout << "set l3 size " << l3_size << std::endl;
-  }
-}
+XPUDeviceContext::~XPUDeviceContext() {}
 
 XPUDeviceContext::XPUDeviceContext(XPUPlace place) : place_(place) {
   int dev_id = -1;

python/paddle/fluid/tests/unittests/xpu/test_pool2d_op_xpu.py

-#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -13,16 +13,20 @@
 # limitations under the License.
 
 from __future__ import print_function
+from __future__ import division
 
 import sys
 sys.path.append("..")
-import paddle.fluid.core as core
 import unittest
 import numpy as np
-from op_test import OpTest
-import paddle
+
+import paddle.fluid.core as core
+from op_test_xpu import XPUOpTest
 import paddle.fluid as fluid
 from paddle.fluid import Program, program_guard
+import paddle
+
+paddle.enable_static()
 
 
 def max_pool2D_forward_naive(x,
@@ -241,7 +245,7 @@ def pool2D_forward_naive(x,
     return out
 
 
-class TestPool2D_Op(OpTest):
+class TestPool2D_Op(XPUOpTest):
     def setUp(self):
         self.op_type = "pool2d"
         self.use_cudnn = False
@@ -265,7 +269,7 @@ class TestPool2D_Op(OpTest):
             input, self.ksize, self.strides, self.paddings, self.global_pool,
             self.ceil_mode, self.exclusive, self.adaptive, self.data_format,
             self.pool_type, self.padding_algorithm).astype(self.dtype)
-        self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(input)}
+        self.inputs = {'X': XPUOpTest.np_dtype_to_fluid_dtype(input)}
 
         self.attrs = {
             'strides': self.strides,
@@ -284,18 +288,20 @@ class TestPool2D_Op(OpTest):
 
         self.outputs = {'Out': output}
 
+    def has_xpu(self):
+        return core.is_compiled_with_xpu()
+
     def test_check_output(self):
-        if paddle.is_compiled_with_xpu():
-            paddle.enable_static()
-            place = paddle.XPUPlace(0)
+        if self.has_xpu():
+            place = core.XPUPlace(0)
             self.check_output_with_place(place)
+        return
 
     def test_check_grad(self):
-        if paddle.is_compiled_with_xpu():
-            paddle.enable_static()
-            place = paddle.XPUPlace(0)
-            self.check_grad_with_place(
-                place, set(['X']), 'Out', max_relative_error=0.07)
+        if self.has_xpu():
+            place = core.XPUPlace(0)
+            self.check_grad_with_place(place, set(['X']), 'Out')
+        return
 
     def init_data_format(self):
         self.data_format = "NCHW"
@@ -315,7 +321,7 @@ class TestPool2D_Op(OpTest):
         self.use_cudnn = False
 
     def init_data_type(self):
-        self.dtype = np.float64
+        self.dtype = np.float32
 
     def init_pool_type(self):
         self.pool_type = "avg"
@@ -334,5 +340,134 @@ class TestPool2D_Op(OpTest):
         self.adaptive = False
 
 
+class TestCase1(TestPool2D_Op):
+    def init_test_case(self):
+        self.ksize = [3, 3]
+        self.strides = [1, 1]
+
+    def init_paddings(self):
+        self.paddings = [0, 0]
+
+    def init_pool_type(self):
+        self.pool_type = "avg"
+        self.pool2D_forward_naive = avg_pool2D_forward_naive
+
+    def init_global_pool(self):
+        self.global_pool = False
+
+    def init_shape(self):
+        self.shape = [2, 3, 7, 7]
+
+
+class TestCase2(TestPool2D_Op):
+    def init_test_case(self):
+        self.ksize = [3, 3]
+        self.strides = [1, 1]
+
+    def init_paddings(self):
+        self.paddings = [1, 1]
+
+    def init_pool_type(self):
+        self.pool_type = "avg"
+        self.pool2D_forward_naive = avg_pool2D_forward_naive
+
+    def init_global_pool(self):
+        self.global_pool = False
+
+    def init_shape(self):
+        self.shape = [2, 3, 7, 7]
+
+
+class TestCase3(TestPool2D_Op):
+    def init_pool_type(self):
+        self.pool_type = "max"
+        self.pool2D_forward_naive = max_pool2D_forward_naive
+
+
+class TestCase4(TestCase1):
+    def init_pool_type(self):
+        self.pool_type = "max"
+        self.pool2D_forward_naive = max_pool2D_forward_naive
+
+
+class TestCase5(TestCase2):
+    def init_pool_type(self):
+        self.pool_type = "max"
+        self.pool2D_forward_naive = max_pool2D_forward_naive
+
+
+class TestPool2D_AsyPadding(TestPool2D_Op):
+    def init_test_case(self):
+        self.ksize = [3, 3]
+        self.strides = [1, 1]
+        self.paddings = [1, 0, 1, 2]
+
+    def init_shape(self):
+        self.shape = [2, 3, 5, 5]
+
+
+class TestCase1_AsyPadding(TestCase1):
+    def init_test_case(self):
+        self.ksize = [3, 3]
+        self.strides = [1, 1]
+        self.paddings = [1, 0, 1, 0]
+
+    def init_shape(self):
+        self.shape = [2, 3, 7, 7]
+
+
+class TestCase2_AsyPadding(TestCase2):
+    def init_test_case(self):
+        self.ksize = [3, 3]
+        self.strides = [1, 1]
+        self.paddings = [1, 2, 1, 2]
+
+    def init_shape(self):
+        self.shape = [2, 3, 7, 7]
+
+
+class TestCase3_AsyPadding(TestCase3):
+    def init_test_case(self):
+        self.ksize = [3, 3]
+        self.strides = [1, 1]
+        self.paddings = [1, 0, 1, 2]
+
+    def init_shape(self):
+        self.shape = [2, 3, 5, 5]
+
+
+class TestCase4_AsyPadding(TestCase4):
+    def init_test_case(self):
+        self.ksize = [3, 3]
+        self.strides = [1, 1]
+        self.paddings = [1, 0, 1, 0]
+
+    def init_shape(self):
+        self.shape = [2, 3, 7, 7]
+
+
+class TestCase5_AsyPadding((TestCase5)):
+    def init_test_case(self):
+        self.ksize = [3, 3]
+        self.strides = [1, 1]
+        self.paddings = [2, 2, 1, 2]
+
+    def init_shape(self):
+        self.shape = [2, 3, 7, 7]
+
+
+class TestAvgInclude_AsyPadding(TestCase2):
+    def init_exclusive(self):
+        self.exclusive = False
+
+    def init_test_case(self):
+        self.ksize = [3, 3]
+        self.strides = [1, 1]
+        self.paddings = [1, 2, 1, 2]
+
+    def init_shape(self):
+        self.shape = [2, 3, 7, 7]
+
+
 if __name__ == '__main__':
     unittest.main()